Agent-based simulation-an application to the new electricity trading arrangements of England and Wales

This paper presents a large-scale application of multiagent evolutionary modeling to the proposed new electricity trading arrangements (NETA) in the UK. This is a detailed plant-by-plant model with an active specification of the demand side of the market. NETA involves a bilateral forward market followed by a balancing mechanism and then an imbalance settlement process. This agent-based simulation model was able to provide pricing and strategic insights, ahead of NETA's actual introduction.

[1]  Gerhard Weiß,et al.  Adaptation and Learning in Multi-Agent Systems: Some Remarks and a Bibliography , 1995, Adaption and Learning in Multi-Agent Systems.

[2]  N. M. Fehr,et al.  SPOT MARKET COMPETITION IN THE UK ELECTRICITY INDUSTRY , 1993 .

[3]  P. Milgrom.  Advances in Economic Theory: Auction theory , 1987 .

[4]  David Carmel,et al.  Learning Models of Intelligent Agents , 1996, AAAI/IAAI, Vol. 1.

[5]  R. Palmer,et al.  Time series properties of an artificial stock market , 1999 .

[6]  A. Rudkevich,et al.  Modeling Electricity Pricing in a Deregulated Generation Industry : The Potential for Oligopoly Pricing in a Poolco , 2022 .

[7]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[8]  Derek Bunn,et al.  Energy Markets Group A Model-Based Comparison of Pool and Bilateral Market Mechanisms for Electricity Trading , 1999 .

[9]  Leigh Tesfatsion,et al.  Market power and efficiency in a computational electricity market with discriminatory double-auction pricing , 2001, IEEE Trans. Evol. Comput..

[10]  R. Green,et al.  Competition in the British Electricity Spot Market , 1992, Journal of Political Economy.

[11]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[12]  Derek W. Bunn,et al.  Experimental analysis of the efficiency of uniform-price versus discriminatory auctions in the England and Wales electricity market ☆ , 2001 .

[13]  Robert B. Wilson.  Game-Theoretic Analysis of Trading Processes. , 1985 .

[14]  Arthur C. Graesser,et al.  Is it an Agent, or Just a Program?: A Taxonomy for Autonomous Agents , 1996, ATAL.

[15]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[16]  Timothy N. Cason,et al.  PRICE FORMATION IN SINGLE CALL MARKETS , 1997 .

[17]  Eithan Ephrati,et al.  Deriving Consensus in Multiagent Systems , 1996, Artif. Intell..

[18]  A. Roth,et al.  Learning in Extensive-Form Games: Experimental Data and Simple Dynamic Models in the Intermediate Term* , 1995 .

[19]  A. Marcet,et al.  Recurrent Hyperinflations and Learning , 2003 .

[20]  A. Roth,et al.  Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria , 1998 .

[21]  Derek W. Bunn,et al.  Model-Based Comparisons of Pool and Bilateral Markets for Electricity , 2000 .

[22]  Darrell Whitley,et al.  Genitor: a different genetic algorithm , 1988 .

[23]  M. Rothkopf.  Daily Repetition: A Neglected Factor in the Analysis of Electricity Auctions , 1999 .

[24]  C. K. Prahalad,et al.  THE DOMINANT LOGIC: RETROSPECTIVE AND EXTENSION , 1995 .

[25]  R. Green,et al.  Increasing Competition in the British Electricity Spot Market , 1996 .